flopy.plot.plotutil module

Module containing helper functions for plotting model data using ModelMap and ModelCrossSection. Functions for plotting shapefiles are also included.

class PlotUtilities

Bases: object

Class which groups a collection of plotting utilities which Flopy and Flopy6 can use to generate map based plots

static centered_specific_discharge(Qx, Qy, Qz, delr, delc, sat_thk)

DEPRECATED. Use postprocessing.get_specific_discharge() instead.

Using the MODFLOW discharge, calculate the cell centered specific discharge by dividing by the flow width and then averaging to the cell center.

Parameters:

• Qx (numpy.ndarray) – MODFLOW ‘flow right face’

• Qy (numpy.ndarray) – MODFLOW ‘flow front face’. The sign on this array will be flipped by this function so that the y axis is positive to north.

• Qz (numpy.ndarray) – MODFLOW ‘flow lower face’. The sign on this array will be flipped by this function so that the z axis is positive in the upward direction.

• delr (numpy.ndarray) – MODFLOW delr array

• delc (numpy.ndarray) – MODFLOW delc array

• sat_thk (numpy.ndarray) – Saturated thickness for each cell

Returns:

(qx, qy, qz) – Specific discharge arrays that have been interpolated to cell centers.

Return type:

tuple of numpy.ndarrays

static saturated_thickness(head, top, botm, laytyp, mask_values=None)

Calculate the saturated thickness.

Parameters:

• head (numpy.ndarray) – head array

• top (numpy.ndarray) – top array of shape (nrow, ncol)

• botm (numpy.ndarray) – botm array of shape (nlay, nrow, ncol)

• laytyp (numpy.ndarray) – confined (0) or convertible (1) of shape (nlay)

• mask_values (list of floats) – If head is one of these values, then set sat to top - bot

Returns:

sat_thk – Saturated thickness of shape (nlay, nrow, ncol).

Return type:

numpy.ndarray

class SwiConcentration(model=None, botm=None, istrat=1, nu=None)

Bases: object

The binary_header class is a class to create headers for MODFLOW binary files

calc_conc(zeta, layer=None)

Calculate concentrations for a given time step using passed zeta.

Parameters:

• zeta (dictionary of numpy arrays) – Dictionary of zeta results. zeta keys are zero-based zeta surfaces.

• layer (int) – Concentration will be calculated for the specified layer. If layer is None, then the concentration will be calculated for all layers. (default is None).

Returns:

conc – Calculated concentration.

Return type:

numpy array

Examples

>>> import flopy
>>> m = flopy.modflow.Modflow.load('test')
>>> c = flopy.plot.SwiConcentration(model=m)
>>> conc = c.calc_conc(z, layer=0)

class UnstructuredPlotUtilities

Bases: object

Collection of unstructured grid and vertex grid compatible plotting helper functions

static arctan2(verts, reverse=False)

Reads 2 dimensional set of verts and orders them using the arctan 2 method

Parameters:

verts (np.array of floats) – Nx2 array of verts

Returns:

verts – Nx2 array of verts

Return type:

np.array of float

static irregular_shape_patch(xverts, yverts)

Patch for vertex cross section plotting when we have an irregular shape type throughout the model grid or multiple shape types.

Parameters:

• xverts (list) – xvertices

• yverts (list) – yvertices

Return type:

xverts, yverts as np.ndarray

static line_intersect_grid(ptsin, xgrid, ygrid)

Uses cross product method to find which cells intersect with the line and then uses the parameterized line equation to caluculate intersection x, y vertex points. Should be quite fast for large model grids!

Parameters:

• pts (list) – list of tuple line vertex pairs (ex. [(1, 0), (10, 0)]

• xgrid (np.array) – model grid x vertices

• ygrid (np.array) – model grid y vertices

Returns:

vdict

Return type:

dict of cell vertices

advanced_package_bc_helper(pkg, modelgrid, kper)

Helper function for plotting boundary conditions from “advanced” packages

Parameters:

• pkg (flopy Package objects) –

• modelgrid (flopy.discretization.Grid object) –

cvfd_to_patch_collection(verts, iverts)

Create a patch collection from control volume vertices and incidence list

Parameters:

• verts (ndarray) – 2d array of x and y points.

• iverts (list of lists) – should be of len(ncells) with a list of vertex numbers for each cell

filter_modpath_by_travel_time(recarray, travel_time)

Helper method for filtering particles by travel time. Used in modpath plotting routines

Parameters:

• recarray (np.recarray) – recarray of modpath particle information

• travel_time (str, float) – travel time logical argument to filter modpath output

Return type:

np.recarray

intersect_modpath_with_crosssection(recarrays, projpts, xvertices, yvertices, projection, ncpl, method='cell', starting=False)

Method to intersect modpath output with a cross-section

Parameters:

• recarrays (list) – list of numpy recarrays

• projpts (dict) – dict of crossectional cell vertices

• xvertices (np.array) – array of modelgrid xvertices

• yvertices (np.array) – array of modelgrid yvertices

• projection (str) – projection direction (x or y)

• ncpl (int) – number of cells per layer (cross sectional version)

• method (str) – intersection method (‘cell’ or ‘all’)

• starting (bool) – modpath starting location flag

Returns:

dict

Return type:

dictionary of intersecting recarrays

parse_modpath_selection_options(ep, direction, selection, selection_direction)

Returns:

plot_cvfd(verts, iverts, ax=None, layer=0, cmap='Dark2', edgecolor='scaled', facecolor='scaled', a=None, masked_values=None, **kwargs)

Generic function for plotting a control volume finite difference grid of information.

Parameters:

• verts (ndarray) – 2d array of x and y points.

• iverts (list of lists) – should be of len(ncells) with a list of vertex number for each cell

• ax (matplotlib.pylot axis) – matplotlib.pyplot axis instance. Default is None

• layer (int) – layer to extract. Used in combination to the optional ncpl parameter. Default is 0

• cmap (string) – Name of colormap to use for polygon shading (default is ‘Dark2’)

• edgecolor (string) – Color name. (Default is ‘scaled’ to scale the edge colors.)

• facecolor (string) – Color name. (Default is ‘scaled’ to scale the face colors.)

• a (numpy.ndarray) – Array to plot.

• masked_values (iterable of floats, ints) – Values to mask.

• kwargs (dictionary) – Keyword arguments that are passed to PatchCollection.set(**kwargs). Some common kwargs would be ‘linewidths’, ‘linestyles’, ‘alpha’, etc.

Returns:

pc

Return type:

matplotlib.collections.PatchCollection

Examples

plot_shapefile(shp, ax=None, radius=500.0, cmap='Dark2', edgecolor='scaled', facecolor='scaled', a=None, masked_values=None, idx=None, **kwargs)

Generic function for plotting a shapefile.

Parameters:

• shp (string or os.PathLike) – Path of the shapefile to plot.

• ax (matplolib.pyplot.axes object) –

• radius (float) – Radius of circle for points. (Default is 500.)

• cmap (string) – Name of colormap to use for polygon shading (default is ‘Dark2’)

• edgecolor (string) – Color name. (Default is ‘scaled’ to scale the edge colors.)

• facecolor (string) – Color name. (Default is ‘scaled’ to scale the face colors.)

• a (numpy.ndarray) – Array to plot.

• masked_values (iterable of floats, ints) – Values to mask.

• idx (iterable int) – A list or array that contains shape numbers to include in the patch collection. Return all shapes if not specified.

• kwargs (dictionary) – Keyword arguments that are passed to PatchCollection.set(**kwargs). Some common kwargs would be ‘linewidths’, ‘linestyles’, ‘alpha’, etc.

Returns:

pc

Return type:

matplotlib.collections.PatchCollection

Examples

reproject_modpath_to_crosssection(idict, projpts, xypts, projection, modelgrid, ncpl, geographic_coords, starting=False)

Method to reproject modpath points onto cross sectional line

Parameters:

• idict (dict) – dictionary of intersecting points

• projpts (dict) – dictionary of cross sectional cells

• xypts (dict) – dictionary of cross sectional line

• projection (str) – projection direction (x or y)

• modelgrid (Grid object) – flopy modelgrid object

• ncpl (int) – number of cells per layer (cross sectional version)

• geographic_coords (bool) – flag for plotting in geographic coordinates

• starting (bool) – flag for modpath position

Return type:

dictionary of projected modpath lines or points

shapefile_extents(shp)

Determine the extents of a shapefile

Parameters:

shp (string) – Name of the shapefile to convert to a PatchCollection.

Returns:

extents – tuple with xmin, xmax, ymin, ymax from shapefile.

Return type:

tuple

Examples

>>> import flopy
>>> fshp = 'myshapefile'
>>> extent = flopy.plot.plotutil.shapefile_extents(fshp)

shapefile_get_vertices(shp)

Get vertices for the features in a shapefile

Parameters:

shp (string) – Name of the shapefile to extract shapefile feature vertices.

Returns:

vertices – Vertices is a list with vertices for each feature in the shapefile. Individual feature vertices are x, y tuples and contained in a list. A list with a single x, y tuple is returned for point shapefiles. A list with multiple x, y tuples is returned for polyline and polygon shapefiles.

Return type:

list

Examples

>>> import flopy
>>> fshp = 'myshapefile'
>>> lines = flopy.plot.plotutil.shapefile_get_vertices(fshp)

shapefile_to_patch_collection(shp: str | PathLike, radius=500.0, idx=None)

Create a patch collection from the shapes in a shapefile

Parameters:

• shp (str or PathLike) – Name of the shapefile to convert to a PatchCollection.

• radius (float) – Radius of circle for points in the shapefile. (Default is 500.)

• idx (iterable int) – A list or array that contains shape numbers to include in the patch collection. Return all shapes if not specified.

Returns:

pc – Patch collection of shapes in the shapefile

Return type:

matplotlib.collections.PatchCollection